Does a function exist (prefferably in matlab) that finds a function in noise

In summary, to find a function in noise, a least-squares fit or taking the fft of the data series and filtering out the noise can be effective methods. It is important to consider the type of noise and whether to use a high pass or low pass filter. If the data series is broadband, a nonlinear noise reduction method such as the LAZY algorithm may be necessary. The meaning of the LAZY algorithm is unclear and further research may be needed.
  • #1
j-lee00
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Does a function exist (prefferably in matlab) that finds a function in noise

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  • #2
Sure, any least-squares fit will do that.
 
  • #3
Curve fit won't work...try taking the fft of the data series, filter out the noise then ifft. The only thing you need to have an idea about is what type of noise is it and whether to use a high pass or low pass filter.
 
  • #4
Dr Transport said:
Curve fit won't work...try taking the fft of the data series, filter out the noise then ifft. The only thing you need to have an idea about is what type of noise is it and whether to use a high pass or low pass filter.

This will work only if the original data series is not broadband, otherwise you need to use nonlinear noise reduction (look for the LAZY algorithm).
 
  • #5
HI, Crosson, can you explain about "LAZY alggorithm", I searched on Google, and still don't understand it's meaning.
Thanks.
 

1. What is the purpose of finding a function in noise?

The purpose of finding a function in noise is to identify and extract the underlying pattern or trend in a dataset that is obscured by random or irrelevant data points, also known as noise.

2. Is there a built-in function in Matlab for finding a function in noise?

Yes, Matlab has a built-in function called findpeaks that can be used to identify and extract peaks or patterns in a dataset, even in the presence of noise.

3. How does the findpeaks function work?

The findpeaks function uses various algorithms and techniques, such as the Savitzky-Golay filter and peak detection methods, to identify and isolate peaks or patterns in a dataset. It also allows for customization of parameters to adjust the sensitivity of the function to noise.

4. Can the findpeaks function be used for all types of noise?

The findpeaks function is designed to work with a wide range of noise types, including Gaussian, white, and nonlinear noise. However, the effectiveness of the function may vary depending on the specific characteristics of the noise and the dataset.

5. Are there any alternatives to using the findpeaks function for finding a function in noise?

Yes, there are other methods and algorithms that can be used to identify and extract patterns in noisy data, such as Fourier transforms, wavelet transforms, and curve fitting techniques. It is important to choose the method that best suits the specific dataset and noise characteristics.

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